├── .gitignore ├── README.md └── information_extraction ├── Doc-level_Relation_Extraction ├── ATLOP │ ├── code │ │ ├── .gitignore │ │ ├── README.md │ │ ├── evaluation.py │ │ ├── long_seq.py │ │ ├── losses.py │ │ ├── model.py │ │ ├── prepro.py │ │ ├── scripts │ │ │ ├── run_bert.sh │ │ │ ├── run_cdr.sh │ │ │ ├── run_gda.sh │ │ │ └── run_roberta.sh │ │ ├── train.py │ │ ├── train_bio.py │ │ └── utils.py │ ├── img │ │ ├── 微信图片_20210328101821.png │ │ ├── 微信图片_20210328102531.png │ │ ├── 微信截图_20210328093836.png │ │ ├── 微信截图_20210328101216.png │ │ ├── 微信截图_20210328102818.png │ │ ├── 微信截图_20210328102904.png │ │ ├── 微信截图_20210328103102.png │ │ ├── 微信截图_20210328103130.png │ │ ├── 微信截图_20210328103542.png │ │ └── 微信截图_20210328103638.png │ └── readme.md ├── AdaptiveFocalLossAndKnowledgeDistillation │ ├── img │ │ ├── 微信截图_20220402233923.png │ │ ├── 微信截图_20220402234456.png │ │ ├── 微信截图_20220402234529.png │ │ ├── 微信截图_20220402234601.png │ │ └── 微信截图_20220402234632.png │ └── readme.md ├── DoubleGraphBasedReasoningforDoclevelRE │ ├── 2009.13752.pdf │ └── readme.md └── RelationPrompt │ ├── img │ ├── 微信截图_20220402233923.png │ ├── 微信截图_20220402234456.png │ ├── 微信截图_20220402234529.png │ ├── 微信截图_20220402234601.png │ ├── 微信截图_20220402234632.png │ ├── 微信截图_20220403100552.png │ ├── 微信截图_20220403102717.png │ ├── 微信截图_20220403102754.png │ ├── 微信截图_20220403103018.png │ ├── 微信截图_20220403103101.png │ ├── 微信截图_20220403103122.png │ ├── 微信截图_20220403103451.png │ ├── 微信截图_20220403104150.png │ └── 微信截图_20220403104853.png │ └── readme.md ├── ERE_study ├── ACL2019_GraphRel │ ├── img │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── pytorch_graph-rel │ │ ├── Dataset │ │ │ └── dummy │ │ ├── README.md │ │ ├── Src │ │ │ └── dummy │ │ ├── imgs │ │ │ ├── experiment.png │ │ │ ├── overview.png │ │ │ ├── poster.png │ │ │ └── result.png │ │ └── model_graph-rel.ipynb │ ├── readme.md │ └── readme_old.md ├── ACL2022_PL-Marker │ ├── img │ │ ├── QQ截图20220417224544.png │ │ ├── QQ截图20220417224627.png │ │ ├── QQ截图20220417224938.png │ │ ├── QQ截图20220417225341.png │ │ ├── QQ截图20220417225420.png │ │ ├── QQ截图20220417225544.png │ │ ├── QQ截图20220417225619.png │ │ ├── QQ截图20220417225712.png │ │ ├── QQ截图20220417225733.png │ │ └── QQ截图20220417225829.png │ └── readme.md ├── COLING20_TPLinker │ ├── img │ │ └── 微信截图_20210123110915.png │ ├── paper.pdf │ └── readme.md ├── EMNLP2021_PFN │ ├── img │ │ └── QQ截图20220417230932.png │ └── readme.md ├── ERE_study │ ├── img │ │ ├── 20230118144508.png │ │ ├── 20230118144818.png │ │ ├── 20230118145321.png │ │ ├── 20230118145446.png │ │ ├── 20230118145819.png │ │ ├── 20230118150801.png │ │ ├── 20230118151430.png │ │ ├── 20230118152544.png │ │ ├── 20230118152642.png │ │ ├── 20230118152827.png │ │ ├── 20230118153341.png │ │ ├── 20230118154407.png │ │ ├── 20230118154430.png │ │ ├── 20230118154448.png │ │ ├── 20230118154612.png │ │ ├── 20230118154653.png │ │ ├── 20230118155046.png │ │ ├── 20230118155149.png │ │ ├── 20230118155415.png │ │ ├── 20230118155723.png │ │ ├── 20230127211800.png │ │ ├── 20230127212043.png │ │ ├── 20230127212622.png │ │ ├── 20230127212920.png │ │ ├── 20230127212946.png │ │ ├── 20230127213051.png │ │ ├── 20230127213112.png │ │ ├── 20230127213948.png │ │ ├── 20230127214134.png │ │ ├── 20230127214307.png │ │ ├── 20230127214419.png │ │ ├── 20230127214944.png │ │ ├── 20230127215026.png │ │ ├── 20230127215226.png │ │ ├── 20230127221241.png │ │ └── 20230127221424.png │ └── 实体关系联合抽取概述.md ├── JointER │ └── readme.md ├── PRGC │ ├── 2106.09895.pdf │ ├── img │ │ ├── 微信截图_20211121195741.png │ │ ├── 微信截图_20211121201005.png │ │ ├── 微信截图_20211121201205.png │ │ ├── 微信截图_20211121201401.png │ │ └── 微信截图_20211121201742.png │ └── readme.md ├── PURE │ ├── 2010.12812.pdf │ ├── img │ │ ├── 20201229161723.png │ │ ├── 20201229164212.png │ │ ├── 20201229164450.png │ │ ├── 20201229164753.png │ │ ├── 20201229164826.png │ │ ├── 20201229170457.png │ │ ├── 20201229170855.png │ │ ├── 20201229171413.png │ │ ├── 20201229171521.png │ │ ├── 20201229172234.png │ │ ├── 20201229172657.png │ │ └── 20201229173558.png │ └── readme.md ├── T2014_joint_extraction │ └── readme.md ├── T20ACL_HBT_su │ ├── 1909.03227.pdf │ ├── img │ │ ├── 1.png │ │ ├── 微信截图_20210509162308.png │ │ ├── 微信截图_20210509163447.png │ │ ├── 微信截图_20210509165814.png │ │ ├── 微信截图_20210509170248.png │ │ ├── 微信截图_20210509170450.png │ │ ├── 微信截图_20210509170733.png │ │ ├── 微信截图_20210509171031.png │ │ ├── 微信截图_20210509171143.png │ │ ├── 微信截图_20210509171245.png │ │ ├── 微信截图_20210509171341.png │ │ ├── 微信截图_20210509171535.png │ │ └── 微信截图_20210509172205.png │ └── readme.md ├── img │ ├── 20200601133014.png │ ├── 20200601133348.png │ ├── 20200601133502.png │ ├── 20200601134211.png │ ├── 20200601134659.png │ ├── 20200601134902.png │ ├── 20200601135019.png │ ├── 20200601135102.png │ ├── 20200601135133.png │ ├── 20200601135209.png │ ├── 20200601193650.png │ ├── 20200602095819.png │ ├── BILOU.png │ ├── ERE.png │ ├── cd.png │ ├── sigmoid.png │ ├── span1.png │ ├── span2.png │ └── 实体关系联合抽取总结.jpg ├── readme.md └── 实体关系联合抽取总结.md ├── NER_study ├── AAAI2022_W2NER │ ├── img │ │ ├── 微信截图_20220309133446.png │ │ ├── 微信截图_20220309133614.png │ │ ├── 微信截图_20220309134009.png │ │ ├── 微信截图_20220309134306.png │ │ ├── 微信截图_20220309134446.png │ │ ├── 微信截图_20220309134530.png │ │ └── 微信截图_20220309134601.png │ └── readme.md ├── ACL2019 │ ├── ACL2019_NERusingPositive-unlabeledLearning │ │ ├── img │ │ │ └── graph1.png │ │ └── readme.md │ ├── ACL2019_TENER │ │ ├── TENER.pdf │ │ ├── img │ │ │ └── 1.png │ │ └── readme.md │ ├── Fine-GrainedEntityTypinginHyperbolicSpace │ │ ├── Fine-Grained Entity Typing in Hyperbolic Space.md │ │ ├── Fine-Grained Entity Typing in Hyperbolic Space.pdf │ │ └── img │ │ │ └── fig1.png │ └── NERusingPositive-unlabeledLearning │ │ ├── Appendix_of_Named_Entity_Recognition_with_Positive_Unlabeled_learning.pdf │ │ └── NERusingPositive-unlabeledLearning.pdf ├── ACL2020_NERasDependencyParsing │ ├── code_pytorch.md │ ├── img │ │ ├── 20201214085740.png │ │ ├── 20201214091032.png │ │ ├── 20201215080914.png │ │ ├── 20201215082820.png │ │ ├── 20201215083025.png │ │ ├── 20201215083059.png │ │ ├── 微信截图_20210507194126.png │ │ ├── 微信截图_20210507194201.png │ │ ├── 微信截图_20210507203317.png │ │ ├── 微信截图_20210507203413.png │ │ ├── 微信截图_20210507203548.png │ │ └── 微信截图_20210507203728.png │ ├── paper.pdf │ └── readme.md ├── ACL2022_Conf_MPU │ ├── img │ │ ├── QQ截图20220828002217.png │ │ ├── QQ截图20220828002607.png │ │ ├── QQ截图20220828002703.png │ │ ├── QQ截图20220828002735.png │ │ ├── QQ截图20220828002839.png │ │ ├── QQ截图20220828003015.png │ │ └── 微信截图_20220826153649.png │ └── readme.md ├── ACL2022_LabelSemanticsForFewShotNER │ ├── img │ │ └── QQ截图20220417231622.png │ └── readme.md ├── ContinualLearningForNER │ ├── img │ │ ├── 微信截图_20210620102816.png │ │ ├── 微信截图_20210620104927.png │ │ ├── 微信截图_20210621085604.png │ │ ├── 微信截图_20210621085753.png │ │ ├── 微信截图_20210621085951.png │ │ ├── 微信截图_20210621090813.png │ │ ├── 微信截图_20210622093933.png │ │ ├── 微信截图_20210622094137.png │ │ ├── 微信截图_20210622094224.png │ │ ├── 微信截图_20210622094716.png │ │ ├── 微信截图_20210622094750.png │ │ ├── 微信截图_20210622095139.png │ │ ├── 微信截图_20210622095520.png │ │ └── 微信截图_20210622095721.png │ └── readme.md ├── DynamicArchitecture │ ├── 1_ACL2018_LatticeLSTM │ │ ├── LatticeLSTM.pdf │ │ ├── img │ │ │ ├── 20200609081729.png │ │ │ ├── LSTM.png │ │ │ ├── LSTM_cul.png │ │ │ ├── LatticeLSTM.png │ │ │ ├── NER.png │ │ │ ├── lattice.png │ │ │ ├── lattice_kj.png │ │ │ ├── lstmcrf.png │ │ │ └── reult.png │ │ └── readme.md │ ├── 2_IJCAI2019_LR_CNN │ │ ├── img │ │ │ ├── 20200615130720.png │ │ │ ├── 20200615131537.png │ │ │ ├── 20200616083045.png │ │ │ ├── 20200616083518.png │ │ │ └── 20200616083725.png │ │ ├── paper.pdf │ │ └── readme.md │ ├── 3_EMNLP2019_CGN │ │ ├── CGN.pdf │ │ ├── img │ │ │ ├── 20200628085215.png │ │ │ ├── 20200630093702.png │ │ │ ├── 20200703084304.png │ │ │ ├── 20200703084458.png │ │ │ ├── 20200703084617.png │ │ │ ├── 20200703084625.png │ │ │ ├── 20200703085000.png │ │ │ ├── 20200703085040.png │ │ │ ├── 20200703085449.png │ │ │ ├── 20200703085750.png │ │ │ ├── 20200703085910.png │ │ │ └── 20200703090253.png │ │ └── readme.md │ ├── 4_EMNLP2019_LGN │ │ ├── LGN.pdf │ │ ├── img │ │ │ ├── 20200706082159.png │ │ │ ├── 20200706083837.png │ │ │ ├── 20200706085117.png │ │ │ ├── 20200706085446.png │ │ │ ├── 20200706085842.png │ │ │ ├── 20200706085916.png │ │ │ ├── 20200706090238.png │ │ │ ├── 20200706090317.png │ │ │ └── 20200706090350.png │ │ └── readme.md │ ├── 5_ACL2020_FLAT │ │ ├── ACL 2020丨【复旦系列】结合词典的中文命名实体识别.pdf │ │ ├── img │ │ │ ├── 20200609092556.png │ │ │ ├── 20200610083057.png │ │ │ ├── 20200610084812.png │ │ │ ├── 20200610091230.png │ │ │ ├── 20200610184316.png │ │ │ ├── 20200611082125.png │ │ │ ├── 20200611082432.png │ │ │ ├── 20200611082858.png │ │ │ ├── 20200611083312.png │ │ │ ├── 20200611083854.png │ │ │ ├── 20200611084332.png │ │ │ └── 20200611090959.png │ │ └── readme.md │ ├── img │ │ ├── 20200609080328.png │ │ ├── 20200609080811.png │ │ ├── 20200609081004.png │ │ ├── 20200609081729.png │ │ ├── 20200609082337.png │ │ ├── 20200609083307.png │ │ ├── 20200609083617.png │ │ ├── 20200609083653.png │ │ ├── 20200609085531.png │ │ ├── 20200609091503.png │ │ ├── 20200609092456.png │ │ ├── 20200609092556.png │ │ ├── 20200609092718.png │ │ ├── 20200609092832.png │ │ ├── 20200609093011.png │ │ ├── 20200609123825.png │ │ ├── 20200609124437.png │ │ ├── 20200609124525.png │ │ ├── 20200609124647.png │ │ ├── 20200609124729.png │ │ ├── 20200609125118.png │ │ ├── 20200609125232.png │ │ ├── 20200609125343.png │ │ ├── 20200609125430.png │ │ ├── 20200609125552.png │ │ ├── 20200609125636.png │ │ ├── 20200609125824.png │ │ ├── 20200609130051.png │ │ ├── 20200609130230.png │ │ ├── 20200609130350.png │ │ ├── 20200609130428.png │ │ ├── graph1.png │ │ ├── graph2.png │ │ ├── graph3.png │ │ ├── graph4.png │ │ ├── graph5.png │ │ ├── graph6.png │ │ ├── graph7.png │ │ ├── graph8.png │ │ ├── graph9.png │ │ └── gs.png │ └── readme.md ├── EMNLP2018_AutoNER │ ├── img │ │ ├── 微信截图_20211113173016.png │ │ ├── 微信截图_20211113192810.png │ │ ├── 微信截图_20211113194459.png │ │ ├── 微信截图_20211113195645.png │ │ ├── 微信截图_20211113195927.png │ │ └── 微信截图_20211113200024.png │ └── readme.md ├── EMNLP2019 │ ├── CrossWeigh从不完善的注释中训练命名实体标注器 │ │ ├── paper.pdf │ │ └── readme.md │ ├── paper.pdf │ ├── paper2.pdf │ ├── 一点注释对引导低资源命名实体识别器有很多好处 │ │ ├── paper.pdf │ │ └── readme.md │ └── 利用词汇知识通过协同图网络进行中文命名实体识别 │ │ ├── img │ │ └── 1.png │ │ ├── paper.pdf │ │ └── readme.md ├── EfficientGlobalPointer │ ├── img │ │ ├── 微信截图_20220215101846.png │ │ ├── 微信截图_20220215102115.png │ │ └── 微信截图_20220215102200.png │ └── readme.md ├── FewShotNER │ ├── img │ │ ├── courgette.log │ │ ├── 微信截图_20211130092122.png │ │ ├── 微信截图_20211130092411.png │ │ ├── 微信截图_20211130092615.png │ │ ├── 微信截图_20211130093942.png │ │ ├── 微信截图_20211130094251.png │ │ ├── 微信截图_20211130094501.png │ │ ├── 微信截图_20211130094537.png │ │ ├── 微信截图_20211130095853.png │ │ ├── 微信截图_20211130100211.png │ │ ├── 微信截图_20211130100535.png │ │ ├── 微信截图_20211130100543.png │ │ └── 微信截图_20211130100641.png │ └── readme.md ├── Few_Shot_NER │ └── Few_Shot_NER_A_Comprehensive_Study │ │ ├── img │ │ ├── 20221112181913.png │ │ ├── 20221112182059.png │ │ └── 微信截图_20221112181121.png │ │ └── readme.md ├── GlobalPointer │ ├── img │ │ ├── 微信截图_20210514214226.png │ │ ├── 微信截图_20220214094434.png │ │ ├── 微信截图_20220214094639.png │ │ ├── 微信截图_20220214100016.png │ │ ├── 微信截图_20220214100727.png │ │ ├── 微信截图_20220214100811.png │ │ ├── 微信截图_20220214101528.png │ │ └── 微信截图_20220214101954.png │ └── readme.md ├── ICLR2021_LEX_BERT │ ├── img │ │ ├── 20210115164908.png │ │ ├── 20210115165224.png │ │ ├── 20210115165357.png │ │ └── 20210115165446.png │ ├── paper.pdf │ └── readme.md ├── NERtrick.md ├── NER如何进行数据增强.xmind ├── NestedNER │ ├── img │ │ ├── 微信截图_20210206110349.png │ │ ├── 微信截图_20210206110429.png │ │ ├── 微信截图_20210206110455.png │ │ ├── 微信截图_20210206110511.png │ │ └── 微信截图_20210206110529.png │ └── readme.md ├── UnlabeledEntityProblem │ ├── img │ │ ├── 20210127173917.png │ │ ├── 20210127174211.png │ │ ├── 20210127174342.png │ │ ├── 20210127175503.png │ │ ├── 20210127175914.png │ │ ├── 20210127180021.png │ │ ├── 20210127180109.png │ │ ├── 20210127180246.png │ │ ├── 20210128110535.png │ │ ├── 20210128111107.png │ │ ├── 20210128111349.png │ │ ├── 20210128111504.png │ │ ├── 20210128111715.png │ │ ├── 20210128111737.png │ │ ├── 20210128130317.png │ │ ├── 20210128130328.png │ │ ├── 20210128131149.png │ │ ├── 20210128131247.png │ │ └── 20210128131337.png │ ├── paper.pdf │ └── readme.md ├── img │ ├── 20200609080328.png │ ├── 20200609080811.png │ ├── 20200609081004.png │ ├── 20200609081729.png │ ├── 20200609082337.png │ ├── 20200609083307.png │ ├── 20200609083617.png │ ├── 20200609083653.png │ ├── 20200609085531.png │ ├── 20200609091503.png │ ├── 20200609092456.png │ ├── 20200609092556.png │ ├── 20200609092718.png │ ├── 20200609092832.png │ ├── 20200609093011.png │ ├── 20200609123825.png │ ├── 20200609124437.png │ ├── 20200609124525.png │ ├── 20200609124647.png │ ├── 20200609124729.png │ ├── 20200609125118.png │ ├── 20200609125232.png │ ├── 20200609125343.png │ ├── 20200609125430.png │ ├── 20200609125552.png │ ├── 20200609125636.png │ ├── 20200609125824.png │ ├── 20200609130051.png │ ├── 20200609130230.png │ ├── 20200609130350.png │ ├── 20200609130428.png │ ├── 20201208200430.png │ ├── 20201208200631.png │ ├── 20201208200933.png │ ├── 20201208201135.png │ ├── 20201208202535.png │ ├── 20201208202913.png │ ├── graph1.png │ ├── graph2.png │ ├── graph3.png │ ├── graph4.png │ ├── graph5.png │ ├── graph6.png │ ├── graph7.png │ ├── graph8.png │ ├── graph9.png │ └── gs.png ├── knnNER │ ├── img │ │ └── 微信截图_20220702144144.png │ ├── paper.pdf │ └── readme.md ├── readme.md └── 电商搜索query-database.xmind ├── NRE_paper_study ├── EMNLP20_NRE_care_liuzhiyuan │ ├── img │ │ └── 20210108090256.png │ ├── paper.pdf │ └── readme.md ├── ERNIE │ └── paper │ │ ├── ERNIE.pdf │ │ └── paper.pdf ├── GraphRel │ ├── GraphRel.pdf │ └── readme_1.md ├── Improving Relation Extraction by Pre-trained Language Representations.pdf ├── JointER │ └── paper.pdf ├── R_BERT │ ├── img │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ └── 4.png │ ├── paper.pdf │ └── readme.md ├── T1_FullySupervisedLearning │ ├── T1_Relation_Classification_via_CDNN │ │ ├── README.md │ │ └── img │ │ │ ├── Lexical-Feature.webp │ │ │ ├── PF.jpg │ │ │ ├── Sentence_Level_Feature.webp │ │ │ ├── frame.jpg │ │ │ └── result.png │ ├── T2_Attention-Based_BiLSTM_for_RC │ │ ├── README.md │ │ ├── code.ipynb │ │ └── img │ │ │ ├── Att-BLSTM.png │ │ │ ├── Attention.png │ │ │ └── result.jpg │ └── T3_RC_via_attention_model_new │ │ ├── README.md │ │ └── img │ │ ├── Input_and_Primary_attention.webp │ │ ├── acnn_structure.png │ │ └── result.webp ├── T2014_joint_extraction │ └── P14-1038.pdf ├── T2016_LSTM_Tree │ ├── 1601.00770.pdf │ ├── img │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ └── 6.png │ └── readme.md ├── T20ACL_HBT_su │ └── 1909.03227.pdf ├── T2_DistantSupervisedLearning │ ├── T1_Piecewise_Convolutional_Neural_Networks │ │ └── img │ │ │ ├── pcnn-1.png │ │ │ ├── pcnn-3.png │ │ │ └── pcnn-4.png │ ├── T2_NRE_with_Selective_Attention_over_Instances │ │ ├── code.ipynb │ │ ├── code_notbag.ipynb │ │ ├── img │ │ │ ├── PerformanceComparison.png │ │ │ ├── Sentence_Encoder.png │ │ │ ├── f3.webp │ │ │ ├── sentence_level_attention_based_CNN.png │ │ │ └── t2.webp │ │ └── tensorflow_function_study.ipynb │ └── img │ │ └── dis-1.png ├── multi-head_selection │ ├── BERT-Based_Multi-Head_Selection │ │ ├── img │ │ │ ├── 微信截图_20210823205217.png │ │ │ └── 微信截图_20210823205625.png │ │ └── readme.md │ └── multi-head_selection │ │ ├── demo │ │ └── demo.py │ │ ├── img │ │ ├── 微信截图_20210823172314.png │ │ ├── 微信截图_20210823173213.png │ │ ├── 微信截图_20210823173530.png │ │ ├── 微信截图_20210823174148.png │ │ ├── 微信截图_20210823174625.png │ │ ├── 微信截图_20210823194824.png │ │ └── 微信截图_20210823200132.png │ │ └── readme.md ├── readme.md └── 开放域知识抽取研究新进展.pdf ├── all_extraction ├── UIE │ ├── img │ │ ├── 微信截图_20220603172410.png │ │ ├── 微信截图_20220603173546.png │ │ ├── 微信截图_20220603173936.png │ │ └── 微信截图_20220603174139.png │ └── readme.md └── USM_AAAI2023 │ ├── img │ ├── 20230118095243.png │ ├── 20230118102427.png │ ├── 20230118104352.png │ ├── 20230118105128.png │ ├── 20230118105811.png │ ├── 20230118110327.png │ ├── 20230118110402.png │ └── 20230118110635.png │ └── readme.md ├── event_extraction └── MLBiNet │ ├── img │ ├── courgette.log │ ├── 微信截图_20210714095648.png │ ├── 微信截图_20210714101357.png │ ├── 微信截图_20210714101905.png │ ├── 微信截图_20210714102148.png │ ├── 微信截图_20210714102517.png │ ├── 微信截图_20210714102921.png │ ├── 微信截图_20210714103124.png │ ├── 微信截图_20210715094254.png │ ├── 微信截图_20210715094337.png │ └── 微信截图_20210715094543.png │ └── readme.md ├── keyword_ex_study ├── KeyBert │ ├── img │ │ ├── 微信截图_20210524100819.png │ │ ├── 微信截图_20210524102047.png │ │ ├── 微信截图_20210524102214.png │ │ └── 微信截图_20210524102248.png │ ├── paper.pdf │ └── readme.md ├── img │ ├── 20200809105640.png │ ├── 20200809110034.png │ ├── 20200809110358.png │ ├── 20200809121756.png │ └── 20200809122329.png ├── kg_one2set │ ├── img │ │ ├── 微信截图_20210911112002.png │ │ ├── 微信截图_20210911114502.png │ │ ├── 微信截图_20210911114524.png │ │ ├── 微信截图_20210911130442.png │ │ ├── 微信截图_20210911170333.png │ │ ├── 微信截图_20210913092241.png │ │ └── 微信截图_20210913092558.png │ └── readme.md └── readme.md ├── readme.md ├── text_mining_study ├── ConcepT_study │ ├── img │ │ └── 微信截图_20200905200354.png │ ├── paper.pdf │ └── readme.md └── readme.md └── word_discovery ├── AutoPhrase ├── img │ ├── 微信截图_20211015221124.png │ ├── 微信截图_20211015223007.png │ ├── 微信截图_20211015223556.png │ ├── 微信截图_20211015225159.png │ └── 微信截图_20211015230203.png ├── paper.pdf └── readme.md ├── data ├── 1.txt ├── 2.txt ├── 3.txt ├── 4.txt ├── 5.txt ├── 6.txt ├── 7.txt └── 8.txt ├── img ├── 微信截图_20210301212242.png ├── 微信截图_20210914085120.png ├── 微信截图_20210915094329.png ├── 微信截图_20211012085339.png └── 微信截图_20211012085542.png ├── readme.md ├── resource └── stopword.txt └── 新词发现.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | # Local .terraform directories 2 | **/.terraform/* 3 | **/w2v_model/ 4 | **/saved/ 5 | **/runs/* 6 | **/.idea/ 7 | **/__pycache__/ 8 | **/.ipynb_checkpoints/ 9 | **/summarys/ 10 | model/ 11 | **/model/ 12 | **/models/ 13 | **/outputs/ 14 | **/saved_models/ 15 | version/ 16 | **/version/ 17 | **/gitinfo/ 18 | **/mywork/* 19 | 20 | T1_Relation_Classification_via_CDNN/saved_models/* 21 | 22 | text_simple_study/RE2_study/simple-effective-text-matching-pytorch/* 23 | 24 | # .tfstate files 25 | *.tfstate 26 | *.tfstate.* 27 | *checkpoint 28 | **/model.ckpt* 29 | *.ckpt* 30 | # .tfvars files 31 | *.tfvars 32 | *.all 33 | *.hdf5 34 | task/.idea/* 35 | *.xml 36 | *.zip 37 | *.rar 38 | *.bin 39 | *.vec 40 | *.model 41 | *.gz 42 | *.bz2 43 | *.word 44 | *.pkl 45 | **/mywork/* 46 | 47 | -------------------------------------------------------------------------------- /information_extraction/Doc-level_Relation_Extraction/ATLOP/code/.gitignore: -------------------------------------------------------------------------------- 1 | 2 | *.json 3 | *.pyc 4 | -------------------------------------------------------------------------------- /information_extraction/Doc-level_Relation_Extraction/ATLOP/code/losses.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | 5 | 6 | class ATLoss(nn.Module): 7 | def __init__(self): 8 | super().__init__() 9 | 10 | def forward(self, logits, labels): 11 | # TH label 12 | th_label = torch.zeros_like(labels, dtype=torch.float).to(labels) 13 | th_label[:, 0] = 1.0 14 | labels[:, 0] = 0.0 15 | 16 | p_mask = labels + th_label 17 | n_mask = 1 - labels 18 | 19 | # Rank positive classes to TH 20 | logit1 = logits - (1 - p_mask) * 1e30 21 | loss1 = -(F.log_softmax(logit1, dim=-1) * labels).sum(1) 22 | 23 | # Rank TH to negative classes 24 | logit2 = logits - (1 - n_mask) * 1e30 25 | loss2 = -(F.log_softmax(logit2, dim=-1) * th_label).sum(1) 26 | 27 | # Sum two parts 28 | loss = loss1 + loss2 29 | loss = loss.mean() 30 | return loss 31 | 32 | def get_label(self, logits, num_labels=-1): 33 | th_logit = logits[:, 0].unsqueeze(1) 34 | output = torch.zeros_like(logits).to(logits) 35 | mask = (logits > th_logit) 36 | if num_labels > 0: 37 | top_v, _ = torch.topk(logits, num_labels, dim=1) 38 | top_v = top_v[:, -1] 39 | mask = (logits >= top_v.unsqueeze(1)) & mask 40 | output[mask] = 1.0 41 | output[:, 0] = (output.sum(1) == 0.).to(logits) 42 | return output 43 | -------------------------------------------------------------------------------- /information_extraction/Doc-level_Relation_Extraction/ATLOP/code/scripts/run_bert.sh: -------------------------------------------------------------------------------- 1 | python train.py --data_dir ./dataset/docred \ 2 | --transformer_type bert \ 3 | --model_name_or_path bert-base-cased \ 4 | --train_file train_annotated.json \ 5 | --dev_file dev.json \ 6 | --test_file test.json \ 7 | --train_batch_size 4 \ 8 | --test_batch_size 8 \ 9 | --gradient_accumulation_steps 1 \ 10 | --num_labels 4 \ 11 | --learning_rate 5e-5 \ 12 | --max_grad_norm 1.0 \ 13 | --warmup_ratio 0.06 \ 14 | --num_train_epochs 30.0 \ 15 | --seed 66 \ 16 | --num_class 97 17 | -------------------------------------------------------------------------------- /information_extraction/Doc-level_Relation_Extraction/ATLOP/code/scripts/run_cdr.sh: -------------------------------------------------------------------------------- 1 | python train_bio.py --data_dir ./dataset/cdr \ 2 | --transformer_type bert \ 3 | --model_name_or_path allenai/scibert_scivocab_cased \ 4 | --train_file train_filter.data \ 5 | --dev_file dev_filter.data \ 6 | --test_file test_filter.data \ 7 | --train_batch_size 4 \ 8 | --test_batch_size 4 \ 9 | --gradient_accumulation_steps 1 \ 10 | --num_labels 1 \ 11 | --learning_rate 2e-5 \ 12 | --max_grad_norm 1.0 \ 13 | --warmup_ratio 0.06 \ 14 | --num_train_epochs 30.0 \ 15 | --seed 66 \ 16 | --num_class 2 17 | -------------------------------------------------------------------------------- /information_extraction/Doc-level_Relation_Extraction/ATLOP/code/scripts/run_gda.sh: -------------------------------------------------------------------------------- 1 | python train_bio.py --data_dir ./dataset/gda \ 2 | --transformer_type bert \ 3 | --model_name_or_path allenai/scibert_scivocab_cased \ 4 | --train_file train.data \ 5 | --dev_file dev.data \ 6 | --test_file test.data \ 7 | --train_batch_size 4 \ 8 | --test_batch_size 8 \ 9 | --gradient_accumulation_steps 4 \ 10 | --num_labels 1 \ 11 | --learning_rate 2e-5 \ 12 | --max_grad_norm 1.0 \ 13 | --warmup_ratio 0.06 \ 14 | --num_train_epochs 10.0 \ 15 | --evaluation_steps 500 \ 16 | --seed 66 \ 17 | --num_class 2 18 | -------------------------------------------------------------------------------- /information_extraction/Doc-level_Relation_Extraction/ATLOP/code/scripts/run_roberta.sh: -------------------------------------------------------------------------------- 1 | python train.py --data_dir ./dataset/docred \ 2 | --transformer_type roberta \ 3 | --model_name_or_path roberta-large \ 4 | --train_file train_annotated.json \ 5 | --dev_file dev.json \ 6 | --test_file test.json \ 7 | --train_batch_size 4 \ 8 | --test_batch_size 8 \ 9 | --gradient_accumulation_steps 1 \ 10 | --num_labels 4 \ 11 | --learning_rate 3e-5 \ 12 | --max_grad_norm 1.0 \ 13 | --warmup_ratio 0.06 \ 14 | --num_train_epochs 30.0 \ 15 | --seed 66 \ 16 | --num_class 97 17 | -------------------------------------------------------------------------------- /information_extraction/Doc-level_Relation_Extraction/ATLOP/code/utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import random 3 | import numpy as np 4 | 5 | 6 | def set_seed(args): 7 | random.seed(args.seed) 8 | np.random.seed(args.seed) 9 | torch.manual_seed(args.seed) 10 | if args.n_gpu > 0 and torch.cuda.is_available(): 11 | torch.cuda.manual_seed_all(args.seed) 12 | 13 | 14 | def collate_fn(batch): 15 | max_len = max([len(f["input_ids"]) for f in batch]) 16 | input_ids = [f["input_ids"] + [0] * (max_len - len(f["input_ids"])) for f in batch] 17 | input_mask = [[1.0] * len(f["input_ids"]) + [0.0] * (max_len - len(f["input_ids"])) for f in batch] 18 | labels = [f["labels"] for f in batch] 19 | entity_pos = [f["entity_pos"] for f in batch] 20 | hts = [f["hts"] for f in batch] 21 | input_ids = torch.tensor(input_ids, dtype=torch.long) 22 | input_mask = torch.tensor(input_mask, dtype=torch.float) 23 | output = (input_ids, input_mask, labels, entity_pos, hts) 24 | return output 25 | -------------------------------------------------------------------------------- /information_extraction/Doc-level_Relation_Extraction/ATLOP/img/微信图片_20210328101821.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/km1994/nlp_paper_study_information_extraction/b24f4b08a343083a7be86f25d8774e8c66337351/information_extraction/Doc-level_Relation_Extraction/ATLOP/img/微信图片_20210328101821.png -------------------------------------------------------------------------------- /information_extraction/Doc-level_Relation_Extraction/ATLOP/img/微信图片_20210328102531.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /information_extraction/Doc-level_Relation_Extraction/DoubleGraphBasedReasoningforDoclevelRE/2009.13752.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/km1994/nlp_paper_study_information_extraction/b24f4b08a343083a7be86f25d8774e8c66337351/information_extraction/Doc-level_Relation_Extraction/DoubleGraphBasedReasoningforDoclevelRE/2009.13752.pdf -------------------------------------------------------------------------------- /information_extraction/Doc-level_Relation_Extraction/DoubleGraphBasedReasoningforDoclevelRE/readme.md: -------------------------------------------------------------------------------- 1 | # 【关于 Double Graph Based Reasoning for Document-level Relation Extraction】 那些的你不知道的事 2 | 3 | > 作者：杨夕 4 | > 5 | > 论文：Double Graph Based Reasoning for Document-level Relation Extraction 6 | > 7 | > 论文地址：https://arxiv.org/abs/2009.13752 8 | > 9 | > github：https://github.com/DreamInvoker/GAIN 10 | 11 | ## 目录 12 | 13 | - [【关于 Double Graph Based Reasoning for Document-level Relation Extraction】 那些的你不知道的事](#关于-double-graph-based-reasoning-for-document-level-relation-extraction-那些的你不知道的事) 14 | - [目录](#目录) 15 | - [背景](#背景) 16 | - [背景](#背景-1) 17 | 18 | ## 背景 19 | 20 | 文档级关系抽取是为了抽取文档中实体之间的关系。与句子级关系抽取不同，它需要对文档中对多个句子进行推理。文章**提出了一种具有双图特征的图聚合推理网络(GAIN)**。GAIN首先构建了一个**异构的提及图**(hMG)来建模文档中不同提及(mention)之间的复杂交互。同时还**构造了实体级图**(EG)，并在此基础上提出了一种新的**路径推理机制**来推断实体之间的关系。在公共数据集DocRED上进行实验，实验结果表明GAIN显著优于之前的技术，比之前的冠军模型在F1上高出2.85。 21 | 22 | ## 背景 23 | 24 | 许多事实知识隐藏信息 25 | 26 | 27 | 28 | 29 | -------------------------------------------------------------------------------- /information_extraction/Doc-level_Relation_Extraction/RelationPrompt/img/微信截图_20220402233923.png: -------------------------------------------------------------------------------- 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A segment-based decoder based on the idea of semi-Markov chain is adopted to the new framework as opposed to traditional token-based tagging. In addition, by virtue of the inexact search, we developed a number of new and effective global features as soft constraints to capture the interdependency among entity mentions and relations. Experiments on Automatic Content Extraction (ACE)1 corpora demonstrate that our joint model significantly outperforms a strong pipelined baseline, which attains better performance than the best-reported end-to-end system. 6 | 7 | 提出了一种增量联合框架，利用结构感知器和有效的集束搜索同时提取提及的实体和关系。新框架采用了基于半马尔可夫链思想的基于分段的解码器，与传统的基于标记的标记方法不同。此外，通过不精确搜索，我们开发了一些新的和有效的全局特性作为软约束来捕获提及的实体和关系之间的相互依赖性。在自动内容提取(ACE)1语料库上的实验表明，我们的联合模型显著优于强流水线方法基线，该基线的性能优于最佳端到端系统。 8 | 9 | 10 | ## 参考资料 11 | 12 | 1. 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每个人都会犯错。在为命名实体识别（NER）整理标签时，人工注释者也是如此。这样的标签错误可能会损害模型训练并干扰模型比较。在本研究中，我们深入研究了一种广泛采用的NER基准数据集CoNLL03 NER。我们能够在大约5.38％的测试句子中识别出标签错误，考虑到最新的测试F1分数已经在93％左右，这是一个很大的比例。因此，我们手动纠正这些标签错误并形成一个更干净的测试集。与原始测试集相比，我们在此经过校正的测试集上对流行模型的重新评估导致更准确的评估。更重要的是，我们提出了一个简单而有效的框架CrossWeigh，以处理NER模型训练期间的标签错误。具体来说，它将训练数据划分为多个折叠，并训练独立的NER模型以识别每个折叠中的潜在错误。然后，它会相应地调整训练数据的权重，以训练最终的NER模型。大量的实验表明，将各种NER模型插入我们在三个数据集上提出的框架中得到了显着改进。 -------------------------------------------------------------------------------- /information_extraction/NER_study/EMNLP2019/paper.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/km1994/nlp_paper_study_information_extraction/b24f4b08a343083a7be86f25d8774e8c66337351/information_extraction/NER_study/EMNLP2019/paper.pdf -------------------------------------------------------------------------------- /information_extraction/NER_study/EMNLP2019/paper2.pdf: 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https://raw.githubusercontent.com/km1994/nlp_paper_study_information_extraction/b24f4b08a343083a7be86f25d8774e8c66337351/information_extraction/NRE_paper_study/EMNLP20_NRE_care_liuzhiyuan/paper.pdf -------------------------------------------------------------------------------- /information_extraction/NRE_paper_study/EMNLP20_NRE_care_liuzhiyuan/readme.md: -------------------------------------------------------------------------------- 1 | # 【关于 关系抽取到底在乎什么】 那些你不知道的事 2 | 3 | > 作者：杨夕 4 | > 5 | > 论文：《Learning from Context or Names? An Empirical Study on Neural Relation Extraction》 6 | > 7 | > 论文地址：https://www.aclweb.org/anthology/2020.emnlp-main.298.pdf 8 | > 9 | > 项目地址：https://github.com/km1994/nlp_paper_study 10 | > 11 | > 个人介绍：大佬们好，我叫杨夕，该项目主要是本人在研读顶会论文和复现经典论文过程中，所见、所思、所想、所闻，可能存在一些理解错误，希望大佬们多多指正。 12 | 13 | ## 目录 14 | 15 | 16 | ## 为什么关系抽取 主要依赖于 【上下文信息】 和 【实体信息】？ 17 | 18 | - 上下文信息：模型需要通过学习实体对的上下文来对实体对进行分类；比如：可以利用 关系模板 「A be founded ... by B」 去 挖掘 (B, be found ,A) ； 19 | 20 | ![](img/20210108090256.png) 21 | 22 | - 实体信息：实体信息主要包括实体类型、实体ID、实体属性信息等，如果实体可以被链接到知识图谱上，那么相关信息也可以被模型所利用。既然关系抽取基于实体pair进行的分类预测，那么实体信息就必不可少了。 23 | 24 | ## 【上下文信息】 和 【实体信息】 哪个对 关系抽取任务 更重要？ 25 | 26 | - 上下文信息 和 实体信息 对于关系模型都很关键 27 | - 实体提及中的类型Type信息很重要 28 | - 上下文信息比实体信息更重要 29 | 30 | ## 参考 31 | 32 | 1. [刘知远老师的“灵魂发问”：关系抽取 到底在乎什么？](https://zhuanlan.zhihu.com/p/340375571) 33 | 34 | 35 | -------------------------------------------------------------------------------- /information_extraction/NRE_paper_study/ERNIE/paper/ERNIE.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/km1994/nlp_paper_study_information_extraction/b24f4b08a343083a7be86f25d8774e8c66337351/information_extraction/NRE_paper_study/ERNIE/paper/ERNIE.pdf -------------------------------------------------------------------------------- /information_extraction/NRE_paper_study/ERNIE/paper/paper.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/km1994/nlp_paper_study_information_extraction/b24f4b08a343083a7be86f25d8774e8c66337351/information_extraction/NRE_paper_study/ERNIE/paper/paper.pdf -------------------------------------------------------------------------------- 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| 4 | [链接](https://github.com/SeoSangwoo/Attention-Based-BiLSTM-relation-extraction) 5 | 6 |

7 | 8 |

9 | 10 | ## Requrements 11 | 12 | * Python (>=3.5) 13 | 14 | * TensorFlow (>=r1.0) 15 | 16 | * scikit-learn (>=0.18) 17 | 18 | ## Reference 19 | * **Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification** (ACL 2016), P Zhou et al. [[paper]](http://www.aclweb.org/anthology/P16-2034) 20 | * roomylee's cnn-relation-extraction repository [[github]](https://github.com/roomylee/cnn-relation-extraction) 21 | 22 | 23 | -------------------------------------------------------------------------------- /information_extraction/NRE_paper_study/T1_FullySupervisedLearning/T2_Attention-Based_BiLSTM_for_RC/img/Att-BLSTM.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/km1994/nlp_paper_study_information_extraction/b24f4b08a343083a7be86f25d8774e8c66337351/information_extraction/NRE_paper_study/T1_FullySupervisedLearning/T2_Attention-Based_BiLSTM_for_RC/img/Att-BLSTM.png -------------------------------------------------------------------------------- /information_extraction/NRE_paper_study/T1_FullySupervisedLearning/T2_Attention-Based_BiLSTM_for_RC/img/Attention.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/km1994/nlp_paper_study_information_extraction/b24f4b08a343083a7be86f25d8774e8c66337351/information_extraction/NRE_paper_study/T1_FullySupervisedLearning/T2_Attention-Based_BiLSTM_for_RC/img/Attention.png -------------------------------------------------------------------------------- /information_extraction/NRE_paper_study/T1_FullySupervisedLearning/T2_Attention-Based_BiLSTM_for_RC/img/result.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/km1994/nlp_paper_study_information_extraction/b24f4b08a343083a7be86f25d8774e8c66337351/information_extraction/NRE_paper_study/T1_FullySupervisedLearning/T2_Attention-Based_BiLSTM_for_RC/img/result.jpg -------------------------------------------------------------------------------- /information_extraction/NRE_paper_study/T1_FullySupervisedLearning/T3_RC_via_attention_model_new/README.md: -------------------------------------------------------------------------------- 1 | # Relation Classification via Attention Model 2 | As you know the attention model can help us to solve many problems.Resently, I have a project which need to recognize the relation from some entities. After reading several paper, I decided to implement this paper: [Relation Classification via Multi-Level Attention CNNs](http://iiis.tsinghua.edu.cn/~weblt/papers/relation-classification.pdf) 3 | I desperately desire to use pytorch to do some awsome things. So it's the only choice for me. And i think you will like it. 4 | 5 | Some of data handling codes are copied from [ACNN](https://github.com/FrankWork/acnn) 6 | 7 | You need an environment: 8 | pytorch 1.0.0 9 | keras & tensorflow (I only used one function which name is to_categorical) 10 | Git this project to your pycharm or other IDE, then edit the acnn_train.py to satisfied your data 11 | # 18.12.17 The Renewed Version 12 | These days, I reviewed the paper again and update my code. But the acc is still low. 13 | 14 | Could somebody can give me some advice? 15 | # Network Structure 16 |

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-------------------------------------------------------------------------------- 1 | # 【关于 BERT-Based Multi-Head Selection 】 那些你不知道的事 2 | 3 | > 作者：杨夕 4 | > 5 | > 论文：《BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction》 6 | > 7 | > 论文地址：https://arxiv.org/abs/1908.05908 8 | > 9 | > 项目地址：https://github.com/km1994/nlp_paper_study 10 | > 11 | > 个人介绍：大佬们好，我叫杨夕，该项目主要是本人在研读顶会论文和复现经典论文过程中，所见、所思、所想、所闻，可能存在一些理解错误，希望大佬们多多指正。 12 | 13 | ## 一、动机 14 | 15 | 1. 之前的工作没有考虑实体关系重叠问题，即一个实体可能与上下文中的多个实体有不同的关系。 16 | 17 | ## 二、创新点 18 | 19 | - 创新点 20 | - 将实体关系联合抽取问题看作是一个 multi-head selection 的问题，即任何一个实体都可能与其他实体存在关系，目的是**解决关系重叠问题** 21 | - 引入 Bert 22 | 23 | ## 三、模型结构介绍 24 | 25 | ![模型整体结构](img/微信截图_20210823205217.png) 26 | 27 | ### 3.1 Bert 28 | 29 | - 介绍：以字符序列作为输入，使用BERT捕获上下文特征 30 | 31 | ### 3.2 CRF层 32 | 33 | - 介绍：用于从句子中提取实体 34 | 35 | ### 3.3 软标签嵌入 层 36 | 37 | - 目标：在CRF逻辑的基础上构建了软标签嵌入，以在实体识别和关系提取之间有效地传递信息 38 | - 思路：以 logits 作为输入来保持每个实体类型的概率。假设N是logits维数，即实体类型的个数，M为标签嵌入矩阵，则第i个字符的软标签嵌入可以形式化为 39 | 40 | ![](img/微信截图_20210823205625.png) 41 | 42 | ### 3.4 Multi-Sigmoid层 43 | 44 | - 目标：为了解决一个实体属于多个三元组的问题，应用了一个Multi-Sigmoid层。 45 | - 思路：将关系分类任务表示为一个多头选择问题，因为句子中的每个token都与其他token有多个关系。将第i个token的soft label embedding feed到两个单独的全连通层中，得到subject和object的表示以预测它们之间的关系。采用multi-sigmoid layer进行关系预测。 46 | 47 | ## 参考 48 | 49 | 1. [阅读分享：BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction](https://blog.csdn.net/baidu_28820009/article/details/106043763) 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | -------------------------------------------------------------------------------- /information_extraction/NRE_paper_study/multi-head_selection/multi-head_selection/demo/demo.py: -------------------------------------------------------------------------------- 1 | from pytorch_transformers import BertConfig 2 | config = BertConfig.from_pretrained("F:/document/datasets/nlpData/pretrain/bert/chinese_wwm_ext_pytorch") 3 | 4 | print(f"config.hidden_size:{config.hidden_size}") 5 | 6 | -------------------------------------------------------------------------------- /information_extraction/NRE_paper_study/multi-head_selection/multi-head_selection/img/微信截图_20210823172314.png: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/km1994/nlp_paper_study_information_extraction/b24f4b08a343083a7be86f25d8774e8c66337351/information_extraction/word_discovery/AutoPhrase/img/微信截图_20211015230203.png -------------------------------------------------------------------------------- /information_extraction/word_discovery/AutoPhrase/paper.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/km1994/nlp_paper_study_information_extraction/b24f4b08a343083a7be86f25d8774e8c66337351/information_extraction/word_discovery/AutoPhrase/paper.pdf -------------------------------------------------------------------------------- /information_extraction/word_discovery/data/1.txt: -------------------------------------------------------------------------------- 1 | ##习近平在第六届东方经济论坛全会开幕式上的致辞（全文） 2 | 尊敬的普京总统， 3 | 　　女士们，先生们，朋友们： 4 | 　　感谢普京总统盛情邀请。很高兴时隔3年再次出席东方经济论坛。 5 | 　　今天是中国人民抗日战争暨世界反法西斯战争胜利76周年纪念日。8月25日，我同普京总统通电话。我们一致认为，国际社会应该坚定捍卫第二次世界大战胜利成果，维护历史真相，坚持以史为鉴、开创未来。 6 | 　　今年6月，我同普京总统成功举行视频会晤，共同宣布《中俄睦邻友好合作条约》延期，就加强中俄战略协作和全方位务实合作等重大问题达成新的共识，新时代中俄全面战略协作伙伴关系动力十足、前景广阔。 7 | 　　当前，世界格局深刻变革，新冠肺炎疫情起伏不定，世界经济艰难复苏。东北亚区域合作既面临严峻挑战，也面临重要机遇。各方应该立足地区，放眼世界，共克时艰，同谋发展。 8 | 　　——我们要在应对疫情挑战方面相互助力，加强疫苗研发、生产合作，为国际社会提供更多公共产品，坚决反对将疫苗和病毒溯源问题政治化，致力于推动构建人类卫生健康共同体。 9 | 　　——我们要在推进互利合作方面持续发力，深化共建“一带一路”同欧亚经济联盟对接合作，支持数字经济创新发展，共同应对全球气候变化，推动地区经济社会发展。 10 | 　　——我们要在维护地区和平稳定方面形成合力，通过对话沟通弥合分歧，凝聚共识，树立共同、综合、合作、可持续的安全观，携手建设和谐安宁的共同家园。 11 | 　　今年是中国共产党百年华诞，中国已经开启全面建设社会主义现代化国家新征程。我们愿同各方一道努力，秉持真正的多边主义，讲信修睦，合作共赢，向着推动构建人类命运共同体的目标稳步迈进。 12 | 　　谢谢大家。 -------------------------------------------------------------------------------- /information_extraction/word_discovery/data/2.txt: -------------------------------------------------------------------------------- 1 | ##王毅国务委员兼外长在东宁要塞博物馆纪念中国人民抗日战争暨世界反法西斯战争胜利76周年活动上的书面致辞 2 | 今天是中国人民抗日战争暨世界反法西斯战争胜利76周年纪念日。 3 | 4 | 　　76年前，中俄两国分别作为亚洲和欧洲主战场，为世界反法西斯战争的伟大胜利付出了巨大民族牺牲，作出了不可磨灭的历史贡献。两国人民相互支持，用生命和鲜血凝结成牢不可破的伟大友谊，为两国世代友好奠定了坚实基础。 5 | 6 | 　　东宁要塞战役是第二次世界大战的最后一战，是中俄人民并肩战斗的重要见证。战役的胜利表明，任何不可一世的反动堡垒都必将被和平与正义的力量所粉碎。 7 | 8 | 　　前事不忘，后事之师。去年，习近平主席同普京总统就纪念世界反法西斯战争胜利75周年互致贺电。不久前，两国元首通电话期间再次强调，中俄在维护历史真相方面加强协作至关重要，特别是在当前依然有人试图否定历史的情况下，中俄更应该坚持以史为鉴、开辟未来。对于国际上开历史倒车的行径，中俄两国人民不会答应，全世界爱好和平的人民也不会答应。 9 | 10 | 　　当前，百年变局与全球疫情叠加共振，世界各国人民的命运又一次紧密地联系在一起。今天举行的纪念活动将唤起人们对和平的向往和坚守，让世界各国人民携起手来，共维和平，共享安宁。 11 | 12 | 　　我们愿同包括俄罗斯在内的国际社会一道： 13 | 14 | 　　——始终做正确二战史观的捍卫者，永远牢记各国人民为维护民族独立和自由、国家主权和尊严建立的伟大功勋，坚决捍卫二战胜利成果和国际公平正义。 15 | 16 | 　　——始终做现行国际秩序的维护者，谨记作为国际社会大家庭一员的使命和担当，合力维护以联合国为核心的国际体系和以国际法为基础的国际秩序。 17 | 18 | 　　——始终做真正多边主义的践行者，推动变革全球治理体制中不公正、不合理的安排，积极促进多边机制完善和发展，矢志不渝构建人类命运共同体。 -------------------------------------------------------------------------------- /information_extraction/word_discovery/data/3.txt: -------------------------------------------------------------------------------- 1 | ##习近平在2021年中国国际服务贸易交易会全球服务贸易峰会上的致辞（全文） 2 | 尊敬的各位来宾， 3 | 4 | 　　女士们，先生们，朋友们： 5 | 6 | 　　大家好！我谨代表中国政府和中国人民，并以我个人名义，向参加2021年中国国际服务贸易交易会的所有嘉宾，表示热烈的欢迎和诚挚的问候！ 7 | 8 | 　　本届服贸会以“数字开启未来，服务促进发展”为主题，相信在各方积极参与下，将成为一届特色鲜明、富有成效的盛会。 9 | 10 | 　　服务贸易是国际贸易的重要组成部分和国际经贸合作的重要领域，在构建新发展格局中具有重要作用。我们愿同各方一道，坚持开放合作、互利共赢，共享服务贸易发展机遇，共促世界经济复苏和增长。 11 | 12 | 　　我们将提高开放水平，在全国推进实施跨境服务贸易负面清单，探索建设国家服务贸易创新发展示范区；我们将扩大合作空间，加大对共建“一带一路”国家服务业发展的支持，同世界共享中国技术发展成果；我们将加强服务领域规则建设，支持北京等地开展国际高水平自由贸易协定规则对接先行先试，打造数字贸易示范区；我们将继续支持中小企业创新发展，深化新三板改革，设立北京证券交易所，打造服务创新型中小企业主阵地。 13 | 14 | 　　女士们、先生们、朋友们！ 15 | 16 | 　　让我们携手抗疫、共克时艰，坚持用和平、发展、合作、共赢的“金钥匙”，破解当前世界经济、国际贸易和投资面临的问题，共创更加美好的未来！ 17 | 18 | 　　谢谢大家。 -------------------------------------------------------------------------------- /information_extraction/word_discovery/data/6.txt: -------------------------------------------------------------------------------- 1 | ##我国已开通建设5G基站99.3万个 覆盖所有地级市 2 | 　31日下午在北京开幕的2021世界5G大会上，工业和信息化部部长肖亚庆介绍，目前我国已开通建设5G基站99.3万个，覆盖全国所有地级市、95%以上的县区和35%的乡镇，5G终端手机连接数超过3.92亿户。 3 | 4 | 　　据了解，本届5G大会以“5G深耕，共融共生”为主题，来自20个国家的1500余位业界专家、学者和企业家以线上或线下的方式参会，大会包括论坛、展览展示、5G应用设计揭榜赛等，全面聚焦共建数字基础设施，致力推动产业合作、跨界融合，搭建5G领域国际交流合作以及产业链协同创新的桥梁，构筑5G深耕行业的展示及推广平台，助力打造5G与其他行业领域共融共生，蓬勃发展的良好局面。 5 | 6 | 　　肖亚庆在开幕式上介绍，目前我国已开通建设5G基站99.3万个，覆盖全国所有地级市、95%以上的县区、35%的乡镇，5G终端手机连接数超过3.92亿户。全国5G应用案例超过1万多个，覆盖了钢铁、电力、矿山等22个国民经济的重要行业和有关领域，形成了一大批丰富多彩的应用场景，成为引领我国高质量发展的新引擎。 7 | 8 | 　　此外，本届5G大会还将推出5G十大应用案例，以成功的应用案例展现5G深耕行业，以科技成果落地实践凸显科技创新赋能社会进步，助力推动5G示范应用场景的复制与推广。 9 | 10 | 　　世界5G大会是全球首个5G领域的国际性盛会，2019年在北京首次举办。世界5G大会见证并助力了5G从加速建设、深化应用到融合发展的进程，吸引了社会各界及众多行业人士的广泛关注与参与，日益成为推动全球5G产业发展和应用场景创新的重要交流平台。 -------------------------------------------------------------------------------- /information_extraction/word_discovery/img/微信截图_20210301212242.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/km1994/nlp_paper_study_information_extraction/b24f4b08a343083a7be86f25d8774e8c66337351/information_extraction/word_discovery/img/微信截图_20210301212242.png 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